Chapter 4 Data statistics

load("data/data.Rdata")

4.1 Sequencing reads statistics

sample_preprocessing %>% 
    summarise(Total=sum(reads_post_filt * 150 / 1000000000) %>% round(2), 
              mean=mean(reads_post_filt * 150 / 1000000000) %>% round(2),
              sd=sd(reads_post_filt * 150 / 1000000000) %>% round(2)) %>%
    unite("Average",mean, sd, sep = " ± ", remove = TRUE) %>%
    tt()

Total	Average
712.18	7.83 ± 8.08

4.2 DNA fractions

sequence_fractions <- read_counts %>%
  pivot_longer(-genome, names_to = "sample", values_to = "value") %>%
  group_by(sample) %>%
  summarise(mags = sum(value)) %>%
    left_join(sample_preprocessing, by = join_by(sample == sample)) %>%
    select(sample,mags,bases_pre_filt,bases_post_filt,host_bases,metagenomic_bases) %>%
    mutate(mags_bases = mags*150) %>%
    mutate(lowqual_bases = bases_pre_filt - bases_post_filt) %>%
    mutate(unmapped_bases = metagenomic_bases - mags_bases) %>%
    mutate(unmapped_bases = ifelse(unmapped_bases < 0, 0, unmapped_bases)) %>%
    select(sample, lowqual_bases, host_bases, unmapped_bases, mags_bases)

sequence_fractions %>%
  mutate_at(vars(-sample), ~./1000000000) %>%
  rename("Sample"=1, "Low quality"=2, "Mapped to host"=3, "Unmapped"=4, "Mapped to MAGs"=5) %>%
  tt()

Sample	Low quality	Mapped to host	Unmapped	Mapped to MAGs
E01	1.9123757	1.7193909	0.50379365	1.83467325
E02	0.4353280	4.2158016	0.99338589	0.02671065
E03	1.3042313	1.7994078	6.42222236	0.01078875
E16	0.4224530	0.1025580	0.20197846	4.93816200
E18	4.7850204	6.9637290	24.05986617	0.52864665
E21	1.0156349	2.6368389	0.90981533	0.10217775
E24	3.7280695	1.1110971	23.81060939	2.34316305
E25	1.5376473	7.9804608	6.00871944	0.85221990
E27	0.4230030	0.0911577	1.95160083	0.44069160
E28	1.6079642	1.0844487	4.47911541	0.19568490
E30	1.7928992	1.4413299	4.00947792	5.12918235
E31	0.6816818	0.0320553	0.50570530	2.84763450
E34	2.1948529	0.8249310	11.67502744	2.54218275
E38	0.9838001	4.3969737	1.28190071	0.01802595
E43	0.4955380	2.8185273	0.82331661	0.00793485
E44	0.4234418	5.9270472	0.44183940	0.24959295
E45	1.5173952	0.8510214	1.95118882	0.84886650
E48	2.3197861	1.9469865	7.44564809	5.90044110
E56	0.5108719	0.0847401	1.09609707	1.82499015
E58	0.4880990	2.1264312	1.36506017	0.11759265
H04	2.5008506	27.4835352	13.14400663	0.03507165
H06	0.2841785	2.0398764	1.46660757	0.01427955
H07	1.4500404	10.9416210	3.39146197	0.01941135
H08	0.5011987	0.2131668	0.57356153	0.45570180
H09	1.2653958	10.8166410	7.00507483	0.15877860
H10	0.7942211	1.3782558	4.03545973	0.11882340
H12	2.5813911	8.4009948	4.51254046	0.84020835
H15	0.1917405	1.5658842	0.74185414	0.00826125
H16	2.1486462	4.0113864	9.56424671	3.69521370
H19	0.5557962	4.2411288	2.50314959	0.02286150
H20	0.9677842	4.3890999	3.78083553	0.07778835
H23	0.9420728	3.9595926	1.30102424	0.29551170
H25	1.0738812	7.7087100	3.42255644	0.68422815
H30	0.4902154	1.3423326	0.75352358	2.29694205
H31	1.8416387	9.7206468	4.43631049	0.48033270
H32	0.5427161	5.8415880	1.38975295	0.06290070
H34	1.3188004	12.4245048	5.12752821	0.02616405
H37	2.5737627	27.8754720	9.15837446	0.25084785
H39	0.2938515	2.4124689	0.75581958	0.02116935
H40	1.1315544	3.8592006	5.83728129	0.42791340
H43	0.7161526	6.3701718	2.50320009	0.03324300
H45	0.9215094	1.5407343	3.94772969	0.81685470
H47	0.9581476	5.6254728	2.15816743	0.32372385
P01	0.5733407	1.8086574	1.33450269	0.76149270
P05	1.1668645	9.3271338	0.63288528	0.36958890
P09	0.7574713	7.1642826	0.52521364	0.04058895
P14	0.5034011	3.8678286	0.03866504	0.64239540
P20	0.7666679	2.0253963	0.68358450	0.54081750
P25	0.2950900	4.5486294	0.50927300	0.38403045
P28	4.4616492	30.8235540	17.16905293	1.83789930
P33	3.3222731	33.2508960	7.86558092	0.43485525
P34	0.3459813	0.8618043	1.84351514	0.01439085
P36	1.2281449	10.5844332	0.28646419	0.71007735
P41	1.6479428	25.4247288	3.56446783	1.35787020
P43	3.4204299	3.2932512	7.78711290	13.30940625
P45	1.6790221	2.9649723	5.42286382	0.12008145
P47	0.4775963	2.8718187	1.06401988	0.80290395
P48	3.0192702	0.9945204	17.82268997	6.15753405
P49	0.8859881	15.7503030	0.32349316	0.16673670
P51	1.8101944	8.2580928	3.46629537	2.64862665
P53	1.5688483	24.5797020	2.14349384	0.12960855
P56	0.9749002	12.3956724	0.19719518	1.01352285
P58	0.7963638	6.4119048	1.11305236	0.07796910
P60	2.3930517	33.2359848	8.57518064	0.20491080
P64	0.2656280	0.3023889	1.17634560	0.47208750
P65	0.4114934	0.2113239	0.89754587	1.90757895
P67	1.7026024	17.3557032	0.62786285	2.02223145
P69	0.4073834	1.8827937	1.76632754	0.23484960
P72	0.2702016	3.9134910	0.57524887	0.20525790
P75	1.4392490	2.6274414	6.26632261	1.62440925
P78	0.9165986	2.0786076	1.22668730	0.48372150
P79	1.7376207	12.1993284	7.73575004	1.52807535

sequence_fractions %>%
    pivot_longer(!sample, names_to = "fraction", values_to = "value") %>%
    mutate(value = value / 1000000000) %>%
    mutate(fraction = factor(fraction, levels = c("lowqual_bases","host_bases","unmapped_bases","mags_bases"))) %>%
    ggplot(., aes(x = sample, y = value, fill=fraction)) +
        geom_bar(position="stack", stat = "identity") +
      scale_fill_manual(name="Sequence type",
                    breaks=c("lowqual_bases","host_bases","unmapped_bases","mags_bases"),
                    labels=c("Low quality","Mapped to host","Unmapped","Mapped to MAGs"),
                    values=c("#CCCCCC", "#bcdee1", "#d8b8a3","#93655c"))+
        labs(x = "Samples", y = "Amount of data (GB)") +
        theme_classic() +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size=6),legend.position = "bottom")

species	mean_host_perc	sd_host_perc	max_host_perc	min_host_perc
Eb	24.0105851	26.0487087	77.523636	0.39599794
Ha	36.4671066	15.3892853	58.926064	6.53771139
Pk	56.5200310	29.2493571	92.136483	1.81265533
NA	0.5625856	0.4379119	1.079399	0.02651141